The Network Scale-up Method (NSUM) is a technique that emerged as an alternative to traditional survey methods and estimates the size of a group from a population using Aggregated Relational Data, which consists of the number of contacts that belong to several groups. NSUM leverages the number of connections of the participants’ surrounding neighborhood, effectively increasing the fraction of the network for which data are available. In addition, the use of ARD preserves the privacy of the participants and therefore increases participation and honesty, mitigating biases of traditional surveys. Due to the advantages of NSUM, it has motivated a wide range of applications in public health, epidemiology, estimation of sensitive groups, and quantifying the impact of disasters.

Although NSUM techniques have been applied to a wide range of problems of public relevance since their introduction nearly four decades ago, several important research challenges remain unresolved. We address these challenges through four complementary research directions. First, we empirically evaluate existing NSUM techniques in an extensive simulation environment. Second, we develop the theoretical foundations of NSUM and ARD methods by analyzing error bounds and methods for estimating temporal trends. Third, we propose robust methods to improve the reliability of NSUM estimates. Finally, we extend the application of NSUM to new domains, including voting intention and the monitoring of peer-to-peer networks.

About Sergio Díaz

Sergio Díaz-Aranda is a final-year PhD student in Mathematical Engineering at the IMDEA Networks Institute and Universidad Carlos III de Madrid, working under the supervision of Prof. Antonio Fernández Anta. He received his MSc and BSc in Mathematics at Universidad Complutense de Madrid, Spain. His research focuses on statistical inference from partial network data in low-resource settings.

This event will be conducted in English